A New Method Based on Belief Rule Base with Balanced Accuracy and Interpretability for Student Achievement Prediction

In the field of education, the accurate prediction of students’ future performance is essential for personalized instruction and efficient allocation of resources. Such predictions not only help education professionals develop targeted educational strategies but also identify students’ learning needs at an early stage so that timely interventions and support can be provided. To gain the trust of educational experts and ensure the practical application value of the prediction results, the prediction methods used must be highly interpretable. However, there are two problems with the current belief rule base (BRB) applied to student performance prediction. First, there is a current lack of effective strategies for enhancing the interpretability of the optimization process. Second, BRB models that overemphasize accuracy tend to exhibit characteristics of black-box models. To overcome these challenges, this paper proposes a new method based on BRB with balanced accuracy and interpretability (BRB-Bai) for student achievement prediction. First, an attribute selection method is proposed to filter out important features associated with student performance. Then, expert knowledge credibility is calculated, and four interpretability strategies are proposed to ensure the interpretability of the model and to achieve a balance between interpretability and accuracy on the basis of expert knowledge credibility. The effectiveness of the proposed model is demonstrated by conducting experiments on the student achievement dataset.